The objectives of planning treatment using intensity modulated radiation therapy (IMRT) are to deliver a uniform and highly conformal prescribed dose of radiation to a PTV, while also minimizing the dose to each nearby OAR. These objectives are invariably in conflict, such that compromises are necessary.
Intensity-modulated radiation therapy (IMRT) treatment planning requires trade-offs to be made between delivering a prescribed treatment dose to the tumour and sparing the surrounding healthy tissues. An IMRT radiotherapy treatment plan specifies a gantry and couch angle for each of several beams, as well as a larger group of parameters that encode the fluence pattern for each beam orientation. Beam angle selection is known to be a non-convex optimization problem, which is fundamentally more difficult than the convex problem of fluence optimization (Hou, Q., J. Wang, et al., 2003, Med. Phys. 30(9): 2360-7). One of the difficulties in treatment plan optimization is that feasible solutions may heavily depend on the optimization parameters such as beam orientations or weights applied to multiple objectives.
Commercial planning software such as Eclipse (Varian Medical Systems, Palo Alto, Calif.) and Pinnacle (Philips Radiation Oncology Solutions, Andover, Mass.) focus mainly on the fluence optimization part of the problem, and do not solve the full multi-objective problem, leaving clinical users to optimize beam orientations by hand. Thus, clinical treatment planning requires human operators (‘treatment planners’ or ‘dosimetrists’) to manually optimize beam orientations, objectives, and/or weights in a time-consuming, trial-and-error process to find some acceptable compromise. This process does not necessarily converge to solutions that are truly optimal, because manual iteration cannot fully explore the enormous parameter space of beam angle combinations and PTV versus OAR dose trade-offs that are possible. Moreover, such an approach samples a highly restricted part of the solution space corresponding to the weights used; many trade-off solutions are never considered unless all possible combinations of weights are explored, which is not feasible in a manual approach. Planning for stereotactic body radiation therapy (SBRT) has many similarities to IMRT treatment planning with notable differences including higher target doses, possibly multiple targets, and often (but not always) more OARs. The present invention improves greatly on the traditional planning process for IMRT and SBRT.
Simultaneous optimization of multiple objectives, using beam orientation and fluence as the optimization parameters requires an optimizer that can escape from local minima in the enormous search space. The genetic algorithm (GA) is an ideal candidate for such a large-scale optimization problem. Beam orientation optimization algorithms incorporating GAs have been presented in the literature (Li et al., Phys. Med. Biol. 49:1915-1932, 2004 and Schreibmann et al., Phys. Med. Biol., 49:747-770, 2004).
Recently, the concept of multi-objective Pareto optimization for IMRT treatment planning has been an active area of research. The concept of Pareto optimality is a mathematical notion that is applied to solve multi-objective problems, where the goal is to optimize more than one fitness function simultaneously and to explore the set of solutions representing trade-offs or compromises between objectives. A Pareto non-dominated solution is defined by the property that no other solution is known that is equivalent or superior in all objectives, and also strictly superior in at least one objective. The Pareto front (or Pareto surface) is the set of all possible Pareto non-dominated solutions, such that it is not mathematically possible to improve performance on any objective function without degrading performance on at least one other objective function. A good multi-objective numerical optimizer is one that efficiently finds a database of non-dominated solutions that approximate and map the structure of the Pareto surface. Multi-objective genetic algorithms are an important class of multi-objective optimizers that can be applied readily to this problem.
Given one or more objective functions for each region-of-interest (ROI), a Pareto non-dominated treatment plan is one for which no other plan is known that is strictly better in at least one objective function while being no worse in every other ROI objective. Currently, commercial treatment planning systems (TPS) are not designed for Pareto optimization. Among treatment planning systems, the present invention is uniquely capable of optimizing several objective functions simultaneously, mapping the detailed structure of their trade-off surface without human interaction during the optimization process, and displaying this information to a human user in an intuitive graphical interface for rapid navigation of a database of optimal solutions to determine the best plan used to treat a patient.